Overview

Dataset statistics

Number of variables11
Number of observations450
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory61.4 KiB
Average record size in memory139.8 B

Variable types

Text2
Categorical2
Numeric7

Alerts

Camere tot is highly overall correlated with Numero stelle and 2 other fieldsHigh correlation
Codice via is highly overall correlated with MunicipioHigh correlation
Municipio is highly overall correlated with Codice viaHigh correlation
Numero stelle is highly overall correlated with Camere tot and 2 other fieldsHigh correlation
Piani totali is highly overall correlated with Camere tot and 2 other fieldsHigh correlation
Posti letto tot is highly overall correlated with Camere tot and 2 other fieldsHigh correlation
Tipo via is highly imbalanced (58.7%)Imbalance
Codice via has 7 (1.6%) zerosZeros
Municipio has 7 (1.6%) zerosZeros

Reproduction

Analysis started2026-01-14 17:58:00.258303
Analysis finished2026-01-14 17:58:06.006332
Duration5.75 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Nome
Text

Distinct436
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size23.2 KiB
2026-01-14T18:58:06.226443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length142
Median length33
Mean length15.057778
Min length3

Characters and Unicode

Total characters6776
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique429 ?
Unique (%)95.3%

Sample

1st rowACCA PALACE
2nd rowALBERGO ACCURSIO
3rd rowALBERGO DEL SOLE
4th rowALBERGO FELICE CASATI
5th rowALBERGO FENICE
ValueCountFrequency (%)
hotel279
25.8%
residence32
 
3.0%
milano26
 
2.4%
albergo17
 
1.6%
unknown9
 
0.8%
di9
 
0.8%
milan9
 
0.8%
la8
 
0.7%
san7
 
0.6%
pensione7
 
0.6%
Other values (534)677
62.7%
2026-01-14T18:58:06.906525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E794
11.7%
O712
10.5%
640
9.4%
L590
8.7%
A584
8.6%
T508
 
7.5%
I436
 
6.4%
N407
 
6.0%
R361
 
5.3%
H333
 
4.9%
Other values (33)1411
20.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)6776
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E794
11.7%
O712
10.5%
640
9.4%
L590
8.7%
A584
8.6%
T508
 
7.5%
I436
 
6.4%
N407
 
6.0%
R361
 
5.3%
H333
 
4.9%
Other values (33)1411
20.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6776
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E794
11.7%
O712
10.5%
640
9.4%
L590
8.7%
A584
8.6%
T508
 
7.5%
I436
 
6.4%
N407
 
6.0%
R361
 
5.3%
H333
 
4.9%
Other values (33)1411
20.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6776
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E794
11.7%
O712
10.5%
640
9.4%
L590
8.7%
A584
8.6%
T508
 
7.5%
I436
 
6.4%
N407
 
6.0%
R361
 
5.3%
H333
 
4.9%
Other values (33)1411
20.8%

Tipologia
Categorical

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size23.2 KiB
ALBERGO
400 
RESIDENCE
50 

Length

Max length9
Median length7
Mean length7.2222222
Min length7

Characters and Unicode

Total characters3250
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRESIDENCE
2nd rowALBERGO
3rd rowALBERGO
4th rowALBERGO
5th rowALBERGO

Common Values

ValueCountFrequency (%)
ALBERGO400
88.9%
RESIDENCE50
 
11.1%

Length

2026-01-14T18:58:07.017268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T18:58:07.114047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
albergo400
88.9%
residence50
 
11.1%

Most occurring characters

ValueCountFrequency (%)
E550
16.9%
R450
13.8%
L400
12.3%
A400
12.3%
B400
12.3%
G400
12.3%
O400
12.3%
S50
 
1.5%
I50
 
1.5%
D50
 
1.5%
Other values (2)100
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)3250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E550
16.9%
R450
13.8%
L400
12.3%
A400
12.3%
B400
12.3%
G400
12.3%
O400
12.3%
S50
 
1.5%
I50
 
1.5%
D50
 
1.5%
Other values (2)100
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E550
16.9%
R450
13.8%
L400
12.3%
A400
12.3%
B400
12.3%
G400
12.3%
O400
12.3%
S50
 
1.5%
I50
 
1.5%
D50
 
1.5%
Other values (2)100
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E550
16.9%
R450
13.8%
L400
12.3%
A400
12.3%
B400
12.3%
G400
12.3%
O400
12.3%
S50
 
1.5%
I50
 
1.5%
D50
 
1.5%
Other values (2)100
 
3.1%

Numero stelle
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7533333
Minimum-1
Maximum6
Zeros0
Zeros (%)0.0%
Negative12
Negative (%)2.7%
Memory size23.6 KiB
2026-01-14T18:58:07.166470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum6
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2955466
Coefficient of variation (CV)0.4705375
Kurtosis0.13872313
Mean2.7533333
Median Absolute Deviation (MAD)1
Skewness-0.69399196
Sum1239
Variance1.678441
MonotonicityNot monotonic
2026-01-14T18:58:07.253238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4147
32.7%
3135
30.0%
183
18.4%
264
14.2%
-112
 
2.7%
57
 
1.6%
62
 
0.4%
ValueCountFrequency (%)
-112
 
2.7%
183
18.4%
264
14.2%
3135
30.0%
4147
32.7%
57
 
1.6%
62
 
0.4%
ValueCountFrequency (%)
62
 
0.4%
57
 
1.6%
4147
32.7%
3135
30.0%
264
14.2%
183
18.4%
-112
 
2.7%

Tipo via
Categorical

Imbalance 

Distinct9
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size23.2 KiB
VIA
339 
VLE
48 
CSO
 
28
PZA
 
24
PLE
 
4
Other values (4)
 
7

Length

Max length7
Median length3
Mean length3.0088889
Min length3

Characters and Unicode

Total characters1354
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.4%

Sample

1st rowVIA
2nd rowVLE
3rd rowVIA
4th rowVIA
5th rowCSO

Common Values

ValueCountFrequency (%)
VIA339
75.3%
VLE48
 
10.7%
CSO28
 
6.2%
PZA24
 
5.3%
PLE4
 
0.9%
LGO3
 
0.7%
GLL2
 
0.4%
ALZ1
 
0.2%
UNKNOWN1
 
0.2%

Length

2026-01-14T18:58:07.364097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-01-14T18:58:07.458322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
via339
75.3%
vle48
 
10.7%
cso28
 
6.2%
pza24
 
5.3%
ple4
 
0.9%
lgo3
 
0.7%
gll2
 
0.4%
alz1
 
0.2%
unknown1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
V387
28.6%
A364
26.9%
I339
25.0%
L60
 
4.4%
E52
 
3.8%
O32
 
2.4%
C28
 
2.1%
S28
 
2.1%
P28
 
2.1%
Z25
 
1.8%
Other values (5)11
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)1354
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
V387
28.6%
A364
26.9%
I339
25.0%
L60
 
4.4%
E52
 
3.8%
O32
 
2.4%
C28
 
2.1%
S28
 
2.1%
P28
 
2.1%
Z25
 
1.8%
Other values (5)11
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1354
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
V387
28.6%
A364
26.9%
I339
25.0%
L60
 
4.4%
E52
 
3.8%
O32
 
2.4%
C28
 
2.1%
S28
 
2.1%
P28
 
2.1%
Z25
 
1.8%
Other values (5)11
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1354
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
V387
28.6%
A364
26.9%
I339
25.0%
L60
 
4.4%
E52
 
3.8%
O32
 
2.4%
C28
 
2.1%
S28
 
2.1%
P28
 
2.1%
Z25
 
1.8%
Other values (5)11
 
0.8%
Distinct307
Distinct (%)68.2%
Missing0
Missing (%)0.0%
Memory size23.2 KiB
2026-01-14T18:58:07.646518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length22
Mean length13.16
Min length4

Characters and Unicode

Total characters5922
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique227 ?
Unique (%)50.4%

Sample

1st rowNICOTERA GIOVANNI
2nd rowCERTOSA
3rd rowSPONTINI GASPARE
4th rowCASATI FELICE
5th rowBUENOS AIRES
ValueCountFrequency (%)
giovanni21
 
2.6%
antonio14
 
1.7%
giuseppe14
 
1.7%
carlo13
 
1.6%
napo10
 
1.2%
della10
 
1.2%
torriani10
 
1.2%
nicola10
 
1.2%
battista8
 
1.0%
dei8
 
1.0%
Other values (430)691
85.4%
2026-01-14T18:58:07.932308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A677
11.4%
O669
11.3%
I659
11.1%
E428
 
7.2%
N401
 
6.8%
R391
 
6.6%
L370
 
6.2%
359
 
6.1%
T279
 
4.7%
C248
 
4.2%
Other values (18)1441
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)5922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A677
11.4%
O669
11.3%
I659
11.1%
E428
 
7.2%
N401
 
6.8%
R391
 
6.6%
L370
 
6.2%
359
 
6.1%
T279
 
4.7%
C248
 
4.2%
Other values (18)1441
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A677
11.4%
O669
11.3%
I659
11.1%
E428
 
7.2%
N401
 
6.8%
R391
 
6.6%
L370
 
6.2%
359
 
6.1%
T279
 
4.7%
C248
 
4.2%
Other values (18)1441
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A677
11.4%
O669
11.3%
I659
11.1%
E428
 
7.2%
N401
 
6.8%
R391
 
6.6%
L370
 
6.2%
359
 
6.1%
T279
 
4.7%
C248
 
4.2%
Other values (18)1441
24.3%

Civico
Real number (ℝ)

Distinct92
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.444444
Minimum1
Maximum371
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2026-01-14T18:58:08.037251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16.25
median15
Q332.75
95-th percentile86.55
Maximum371
Range370
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation37.182482
Coefficient of variation (CV)1.3548273
Kurtosis27.115821
Mean27.444444
Median Absolute Deviation (MAD)10
Skewness4.1730042
Sum12350
Variance1382.537
MonotonicityNot monotonic
2026-01-14T18:58:08.154891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
623
 
5.1%
223
 
5.1%
422
 
4.9%
117
 
3.8%
1217
 
3.8%
1017
 
3.8%
1815
 
3.3%
315
 
3.3%
1414
 
3.1%
814
 
3.1%
Other values (82)273
60.7%
ValueCountFrequency (%)
117
3.8%
223
5.1%
315
3.3%
422
4.9%
513
2.9%
623
5.1%
712
2.7%
814
3.1%
912
2.7%
1017
3.8%
ValueCountFrequency (%)
3711
0.2%
3001
0.2%
2781
0.2%
1701
0.2%
1531
0.2%
1431
0.2%
1391
0.2%
1341
0.2%
1322
0.4%
1251
0.2%

Codice via
Real number (ℝ)

High correlation  Zeros 

Distinct303
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3065.6089
Minimum0
Maximum7505
Zeros7
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2026-01-14T18:58:08.276911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile302.25
Q11339.5
median2246
Q34898.5
95-th percentile7188
Maximum7505
Range7505
Interquartile range (IQR)3559

Descriptive statistics

Standard deviation2172.0713
Coefficient of variation (CV)0.70852852
Kurtosis-0.76208895
Mean3065.6089
Median Absolute Deviation (MAD)1101
Skewness0.67076453
Sum1379524
Variance4717893.9
MonotonicityNot monotonic
2026-01-14T18:58:08.415617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
212610
 
2.2%
22298
 
1.8%
07
 
1.6%
21347
 
1.6%
21295
 
1.1%
31155
 
1.1%
10554
 
0.9%
24004
 
0.9%
71884
 
0.9%
31834
 
0.9%
Other values (293)392
87.1%
ValueCountFrequency (%)
07
1.6%
11
 
0.2%
1051
 
0.2%
1151
 
0.2%
1231
 
0.2%
1391
 
0.2%
1441
 
0.2%
1461
 
0.2%
1901
 
0.2%
2072
 
0.4%
ValueCountFrequency (%)
75051
0.2%
75001
0.2%
74251
0.2%
74201
0.2%
73961
0.2%
73901
0.2%
73821
0.2%
73601
0.2%
72761
0.2%
72721
0.2%

Municipio
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0466667
Minimum0
Maximum9
Zeros7
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2026-01-14T18:58:08.518167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q36.75
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation2.6503855
Coefficient of variation (CV)0.65495524
Kurtosis-0.99332059
Mean4.0466667
Median Absolute Deviation (MAD)2
Skewness0.57958017
Sum1821
Variance7.0245434
MonotonicityNot monotonic
2026-01-14T18:58:08.607288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3110
24.4%
274
16.4%
171
15.8%
845
10.0%
935
 
7.8%
733
 
7.3%
429
 
6.4%
526
 
5.8%
620
 
4.4%
07
 
1.6%
ValueCountFrequency (%)
07
 
1.6%
171
15.8%
274
16.4%
3110
24.4%
429
 
6.4%
526
 
5.8%
620
 
4.4%
733
 
7.3%
845
10.0%
935
 
7.8%
ValueCountFrequency (%)
935
 
7.8%
845
10.0%
733
 
7.3%
620
 
4.4%
526
 
5.8%
429
 
6.4%
3110
24.4%
274
16.4%
171
15.8%
07
 
1.6%

Camere tot
Real number (ℝ)

High correlation 

Distinct148
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.002222
Minimum7
Maximum439
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2026-01-14T18:58:08.707531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile9.45
Q116
median32
Q372
95-th percentile231.3
Maximum439
Range432
Interquartile range (IQR)56

Descriptive statistics

Standard deviation70.441133
Coefficient of variation (CV)1.1739754
Kurtosis7.2530647
Mean60.002222
Median Absolute Deviation (MAD)21
Skewness2.5064947
Sum27001
Variance4961.9532
MonotonicityNot monotonic
2026-01-14T18:58:08.825487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1024
 
5.3%
1115
 
3.3%
1415
 
3.3%
1514
 
3.1%
1311
 
2.4%
1611
 
2.4%
1811
 
2.4%
1710
 
2.2%
710
 
2.2%
1210
 
2.2%
Other values (138)319
70.9%
ValueCountFrequency (%)
710
2.2%
86
 
1.3%
97
 
1.6%
1024
5.3%
1115
3.3%
1210
2.2%
1311
2.4%
1415
3.3%
1514
3.1%
1611
2.4%
ValueCountFrequency (%)
4391
0.2%
4231
0.2%
4201
0.2%
3281
0.2%
3271
0.2%
3231
0.2%
3201
0.2%
3131
0.2%
3051
0.2%
3021
0.2%

Piani totali
Real number (ℝ)

High correlation 

Distinct14
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6444444
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2026-01-14T18:58:08.907776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q36
95-th percentile9
Maximum17
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.201999
Coefficient of variation (CV)0.47411463
Kurtosis4.299511
Mean4.6444444
Median Absolute Deviation (MAD)1
Skewness1.4190067
Sum2090
Variance4.8487998
MonotonicityNot monotonic
2026-01-14T18:58:09.017213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3115
25.6%
497
21.6%
574
16.4%
646
 
10.2%
734
 
7.6%
222
 
4.9%
118
 
4.0%
818
 
4.0%
914
 
3.1%
106
 
1.3%
Other values (4)6
 
1.3%
ValueCountFrequency (%)
118
 
4.0%
222
 
4.9%
3115
25.6%
497
21.6%
574
16.4%
646
 
10.2%
734
 
7.6%
818
 
4.0%
914
 
3.1%
106
 
1.3%
ValueCountFrequency (%)
172
 
0.4%
131
 
0.2%
121
 
0.2%
112
 
0.4%
106
 
1.3%
914
 
3.1%
818
 
4.0%
734
7.6%
646
10.2%
574
16.4%

Posti letto tot
Real number (ℝ)

High correlation 

Distinct196
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.12889
Minimum7
Maximum922
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.6 KiB
2026-01-14T18:58:09.171529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile17
Q125
median60
Q3137.25
95-th percentile440.1
Maximum922
Range915
Interquartile range (IQR)112.25

Descriptive statistics

Standard deviation141.01355
Coefficient of variation (CV)1.235564
Kurtosis8.0612704
Mean114.12889
Median Absolute Deviation (MAD)38
Skewness2.6096277
Sum51358
Variance19884.821
MonotonicityNot monotonic
2026-01-14T18:58:09.403174image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2535
 
7.8%
2015
 
3.3%
2414
 
3.1%
2311
 
2.4%
329
 
2.0%
219
 
2.0%
228
 
1.8%
168
 
1.8%
997
 
1.6%
487
 
1.6%
Other values (186)327
72.7%
ValueCountFrequency (%)
71
 
0.2%
91
 
0.2%
101
 
0.2%
111
 
0.2%
124
0.9%
143
 
0.7%
153
 
0.7%
168
1.8%
176
1.3%
186
1.3%
ValueCountFrequency (%)
9221
0.2%
8641
0.2%
7921
0.2%
7361
0.2%
7251
0.2%
6501
0.2%
6461
0.2%
6361
0.2%
6231
0.2%
5771
0.2%

Interactions

2026-01-14T18:58:05.024392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:00.707295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:01.582230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:02.306364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:03.019368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:03.761244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:04.371511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:05.120970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:00.913973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:01.690412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:02.406907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:03.113644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:03.870028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:04.453181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:05.221207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:01.022824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:01.805152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:02.521076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:03.366237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:03.959222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:04.561448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:05.317036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:01.153075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:01.914136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:02.635731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:03.448741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:04.034462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:04.665078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:05.404865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:01.266908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:02.015593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:02.727726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:03.529736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:04.128408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:04.749701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:05.499042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:01.376771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:02.112299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:02.824304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:03.601832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:04.204794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:04.833514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:05.582276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:01.486761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:02.202017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:02.915834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:03.697995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:04.281121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-01-14T18:58:04.932994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-01-14T18:58:09.542917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Camere totCivicoCodice viaMunicipioNumero stellePiani totaliPosti letto totTipo viaTipologia
Camere tot1.000-0.073-0.165-0.0810.7120.8160.9820.1000.042
Civico-0.0731.0000.1290.092-0.135-0.080-0.0790.0000.000
Codice via-0.1650.1291.0000.588-0.135-0.161-0.1580.0850.012
Municipio-0.0810.0920.5881.000-0.065-0.132-0.0840.1620.168
Numero stelle0.712-0.135-0.135-0.0651.0000.7130.7070.0000.162
Piani totali0.816-0.080-0.161-0.1320.7131.0000.8040.0260.106
Posti letto tot0.982-0.079-0.158-0.0840.7070.8041.0000.1230.071
Tipo via0.1000.0000.0850.1620.0000.0260.1231.0000.000
Tipologia0.0420.0000.0120.1680.1620.1060.0710.0001.000

Missing values

2026-01-14T18:58:05.772377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-01-14T18:58:05.924696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

NomeTipologiaNumero stelleTipo viaNome viaCivicoCodice viaMunicipioCamere totPiani totaliPosti letto tot
244ACCA PALACERESIDENCE4VIANICOTERA GIOVANNI91508941582
412ALBERGO ACCURSIOALBERGO3VLECERTOSA887174827439
353ALBERGO DEL SOLEALBERGO1VIASPONTINI GASPARE62141317240
104ALBERGO FELICE CASATIALBERGO4VIACASATI FELICE1821223993145
4ALBERGO FENICEALBERGO3CSOBUENOS AIRES22129346498
289ALBERGO IRIDEALBERGO2VIAPORPORA NICOLA ANTONIO1702229311319
111ALBERGO LA PACEALBERGO1VIACATALANI ALFREDO692425320633
191ALBERGO LARIOALBERGO1VIALARIO40117197317
429ALBERGO LOMBARDIAALBERGO3VLELOMBARDIA7424003965157
339ALBERGO MARTEALBERGO2VIASFORZA ASCANIO815201515325
NomeTipologiaNumero stelleTipo viaNome viaCivicoCodice viaMunicipioCamere totPiani totaliPosti letto tot
166ZEFIROALBERGO4VIAGALLINA GIACINTO123105354592
9UNKNOWNALBERGO5CSOCONCORDIA131163776180
62UNKNOWNALBERGO3PZASANT' EUSTORGIO25186122438
87UNKNOWNRESIDENCE2VIABREMBO274219555499
148UNKNOWNALBERGO4VIAFELTRE192652338570
185UNKNOWNRESIDENCE2VIAIPPODROMO8649281165232
248UNKNOWNALBERGO4VIAORSEOLO PIETRO151136595101
326UNKNOWNALBERGO4VIASAN TOMASO8723111422
355UNKNOWNALBERGO4VIASTEPHENSON GIORGIO55002569512
388UNKNOWNALBERGO4VIAVENEZIA GIULIA9750081156230